HeyJaredInflection Point AI
Inflection Point: Digital Intelligence Podcast From the leaders who built it. Enterprise-focused conversations with the CIOs, CISOs, founders, and executives shaping Digital Intelligence.
- Indexed videos, last 90 days
- 93
- Latest publication
- Sep 17, 2026
- Audience
- ~86 subscribers
- Earliest in this view
- Jul 8, 2026
Latest videos
When CVSS Signals Stop Meaning Anything (opens the original)
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One of CVSS's core attributes is attack complexity — a metric built around human attackers. But automated attackers don't have "complexity" the same way. The other signal teams leaned on was exploit availability: no POC, no urgency, treat it as theoretical. Except AI models are now finding POC and active exploits far faster than before. The two traditional signals that told teams "this can wait" have quietly lost their meaning — and the noise-to-signal ratio is worse than it's ever been. We disc
Backlog vs. Response: When SLAs Aren't Enough (opens the original)
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Moving from a monthly cadence, backlog-management, SLA-driven program to something with a response layer built in — instant response, exposure response. Not everything belongs in a queue waiting its turn. Some things need a signal that says: this isn't a backlog task, this is a threat, act on it now. The shift isn't just faster triage — it's a different category of work sitting alongside the backlog, not replacing it. We discussed: ◼ Why SLA-driven backlogs work for routine issues but fail for a
Treat Inconclusive as Exploitable (opens the original)
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The clearest illustration of why grounding matters is a flight. Ask a model when your flight to San Diego leaves and it will tell you flights usually leave between five and six — plausible, well-formed, and useless. Connect it to your calendar and the answer becomes 5:45, terminal two, with the flight number. Same model, same question. The difference is that one is generating from a pattern and the other is reading from your data. That gap is the entire argument for the data layer, and it's why
The model didn't get smarter. It got your calendar (opens the original)
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The clearest illustration of why grounding matters is a flight. Ask a model when your flight to San Diego leaves and it will tell you flights usually leave between five and six — plausible, well-formed, and useless. Connect it to your calendar and the answer becomes 5:45, terminal two, with the flight number. Same model, same question. The difference is that one is generating from a pattern and the other is reading from your data. That gap is the entire argument for the data layer, and it's why
How AI Rewrote Product Management In 6 Months (opens the original)
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In just six months, the wall between product, design, and engineering collapsed — and one person can now do all three! What happens to product management when AI lets a single person own the requirements, the design, and the code? The blurring of three roles into one — and why no startup can raise money on a slide deck anymore. We discussed: ◼ How AI collapsed three roles into one in months ◼ Why a product manager now lives across the whole stack ◼ How checking code first sharpens every product
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~86 subscribers
Measured Sep 19, 2026
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